Single incision fasciotomy for acute compartment syndrome of the leg: A systematic review of the literature
Bibliographic record
Abstract
Purpose: To review published literature assessing clinical outcomes and complication rates of single incision fasciotomy in fractures of the lower leg. Methods: We searched PubMed and EMBASE for articles published through July 5, 2021, using the terms "single incision fasciotomy", "acute compartment syndrome of the limbs", "compartment syndrome fasciotomy", and "(compartment syndrome fasciotomy) AND (incision)". The inclusion criteria were studies of Level I to IV evidence in English, published in 1970 or later, involving human subjects, reporting clinical outcomes of single incision fasciotomy performed in cases of acute compartment syndrome in lower leg fractures, including at least 1 patient. Results: Among the 3040 combined total results, 11 primary studies met our inclusion criteria. Adequate and safe compartment release was achieved with single-incision technique. No significant difference was found in terms of complications such as infection and non-union. Conclusions: The comparative efficacy and safety of single-incision fasciotomy is relatively equal to the two-incision techniques when evaluated in the literature. However, double-incision fasciotomy remains the predominant surgical technique, widely preferred by surgeons due to the familiarity with the technique and ease of full compartment release. In addition to the actual fasciotomy procedure, data suggests that operative timing, closure and fixation techniques can significantly impact patient outcomes. These findings may be used to guide the orthopedic community when determining the optimal incision-type to use in acute compartment syndrome emergencies for lower-extremity fracture cases in conjunction with closure and fixation techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".